class_docstr: EfficientNet is a very efficient image-classification model. Trained on ImageNet.
  
  Attributes:
      model_path: Any efficientnet model (smaller to bigger) from efficientnet-b0 to efficientnet-b7
      init_model: Callable that initialises the model from the model_path
      name: string identifier for the model. Lowercase letters and numbers.
          No spaces/special characters except dashes.
      description: String description of the model.
      tasks: List of supported task strings
      details: Dictionary of additional details about the model
      device: Device for model. Defaults to "cuda" if available.
code: def process_batch(self, params, task="image-classification"):
      image = params["image"]
      image = Image.open(image)
      image = self.process_image(image).squeeze(0)
      return image
  
imports: import torch
  from PIL import Image
  from typing import Union, List, Dict
  from efficientnet_pytorch import EfficientNet
  from torchvision import transforms
  from torchvision.datasets import ImageFolder
  from random import shuffle
  from backprop.models import PathModel
  from backprop.utils.download import download
  from pytorch_lightning.callbacks.early_stopping import EarlyStopping
  from torch.utils.data import DataLoader
  from functools import partial
  import torch.nn.functional
  import pytorch_lightning
  import json
  import os
  from io import BytesIO
  import base64
  from backprop.utils.helpers import base64_to_img
label_desc: Use image node_id 7 for calling the EfficientNet obj's underlying member method code with example usage: obj.process_batch(params, task) and returns: image